Evidence map›Paper›PMID 37924208›Full record

ArticleBiophysical journal2023

Fitting high-resolution electron density maps from atomic models to solution scattering data.

Sarah R Chamberlain, Stephen Moore, Thomas D Grant

Abstract read
In one paragraph

Article in Biophysical journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Fast calculation of small-angle scattering profiles of dense protein solutions modeled at the all-atom level.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  6. Article
  7. Validation of electron-microscopy maps using solution small-angle X-ray scattering.Acta crystallographica. Section D, Structural biology · 2024
    Article
  8. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Sarah R ChamberlainDepartment of Structural Biology, Jacobs School of Medicine and Biomedical Sciences, SUNY University at Buffalo, Buffalo, New York.
Stephen MooreDepartment of Structural Biology, Jacobs School of Medicine and Biomedical Sciences, SUNY University at Buffalo, Buffalo, New York.
Thomas D GrantDepartment of Structural Biology, Jacobs School of Medicine and Biomedical Sciences, SUNY University at Buffalo, Buffalo, New York. Electronic address: tdgrant@buffalo.edu.

Funding

High-resolution molecular recognition of ligands using solution X-ray scatteringR01GM133998 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI GRANT, THOMAS D. · 2019 to 2022
$1.3M
NIGMS NIH HHS R01 GM133998
6 · The paper itself

Abstract

Solution scattering techniques, such as small- and wide-angle X-ray scattering (SWAXS), provide valuable insights into the structure and dynamics of biological macromolecules in solution. In this study, we present an approach to accurately predict solution X-ray scattering profiles at wide angles from atomic models by generating high-resolution electron density maps. Our method accounts for the excluded volume of bulk solvent by calculating unique adjusted atomic volumes directly from the atomic coordinates. This approach eliminates the need for one of the free fitting parameters commonly used in existing algorithms, resulting in improved accuracy of the calculated SWAXS profile. An implicit model of the hydration shell is generated that uses the form factor of water. Two parameters, namely the bulk solvent density and the mean hydration shell contrast, are adjusted to best fit the data. Results using eight publicly available SWAXS profiles show high-quality fits to the data. In each case, the optimized parameter values show small adjustments demonstrating that the default values are close to the true solution. Disabling parameter optimization produces significantly more accurate predicted scattering profiles compared to the leading software. The algorithm is computationally efficient, comparable to the leading software and up to 10 times faster for large molecules. The algorithm is encoded in a command line script called denss.pdb2mrc.py and is available open source as part of the DENSS v1.7.0 software package. In addition to improving the ability to compare atomic models to experimental SWAXS data, these developments pave the way for increasing the accuracy of modeling algorithms using SWAXS data and decreasing the risk of overfitting.

Indexed as

ElectronsWaterScattering, Small AngleSolventsX-Ray DiffractionSolventsWater

Identifiers

PMID37924208
PMCPMC10719074

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.